Vaex is a program and Python library to visualize and explore large tabular datasets. It can calculate statistics.
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Vaex is a program and Python library to visualize and explore large tabular datasets. It can calculate statistics.
Read moreHoloViews is an open-source Python library designed to make data analysis and visualization seamless and simple.
Read moreDask is a flexible parallel computing library for analytic computing. It takes a Python job and shares it across multiple systems.
Read moreOptimus is the missing framework to profile, clean, process and do ML in a distributed fashion using Apache Spark (PySpark).
Read moreyt is an open-source Python package for analyzing and visualizing volumetric data. yt focuses on driving physically-meaningful inquiry.
Read moreAWS Data Wrangler extends the power of Pandas library to AWS connecting DataFrames and AWS data related services.
Read moreThe R Project for Statistical Computing (R) is a free software environment for statistical computing and graphics.
Read moreMOA is a software environment for implementing algorithms and running experiments for online learning from evolving data streams.
Read moreOrange is a component-based framework for machine learning and data mining. It includes a range of data visualization, and exploration.
Read moreEnvironment for DeveLoping KDD-Applications Supported by Index-Structures (ELKI) is a data mining software framework.
Read moreDataMelt is an environment for scientific computation, data analysis and data visualization.
Read moreWeka (Waikato Environment for Knowledge Analysis) is a comprehensive popular suite of machine learning software written in Java.
Read moreRattle provides a Gnome based open source interface to R functionality for binary classification tasks and data mining.
Read moreSU2 is a suite of tools for the numerical solution of partial differential equations (PDE) and performing PDE constrained optimization.
Read moreSherpa is a Monte Carlo event generator for the Simulation of High-Energy Reactions of PArticles.
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